For most, Distress is generally lowest after a daily total of 2400 milliliters of Water intake over the previous 7 days.
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People with higher Water intake usually have lower Distress
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Water on each day of the week.
This chart shows the typical value recorded for Water for each month of the year.
Each column represents the number of days this value occurred.
This chart shows the typical value recorded for Distress on each day of the week.
This chart shows the typical value recorded for Distress for each month of the year.


Aggregated data from 11 study participants suggests with a medium degree of confidence (p=0.21476420933719, 95% CI -0.509 to 0.686) that Water (mL) has a very weakly positive predictive relationship (R=0.09) with Distress. The highest quartile of Distress measurements were observed following an average 1 milliliters Water (mL) per day. The lowest quartile of Distress measurements were observed following an average 1977.9743969997 mL Water (mL) per day.


The objective of this study is to determine the nature of the relationship (if any) between Water (mL) and Distress. Additionally, we attempt to determine the Water (mL) values most likely to produce optimal Distress values.

Participant Instructions

Get Fitbit here and use it to record your Water. Once you have a Fitbit account, you can import your data from the Import Data page. Your data will automatically be imported and analyzed.
Record your Distress daily in the reminder inbox or using the interactive web or mobile notifications.


This study is based on data donated by 11 participants. Thus, the study design is equivalent to the aggregation of 11 separate n=1 observational natural experiments.

Data Analysis

Water Pre-Processing
Water measurement values below 0 milliliters were assumed erroneous and removed. No maximum allowed measurement value was defined for Water. It was assumed that any gaps in Water data were unrecorded 0 milliliters measurement values.
Water Analysis Settings

Distress Pre-Processing
Distress measurement values below 1 out of 5 were assumed erroneous and removed. Distress measurement values above 5 out of 5 were assumed erroneous and removed. No missing data filling value was defined for Distress so any gaps in data were just not analyzed instead of assuming zero values for those times.
Distress Analysis Settings

Predictive Analytics
It was assumed that 0.5 hours would pass before a change in Water (mL) would produce an observable change in Distress. It was assumed that Water (mL) could produce an observable change in Distress for as much as 7 days after the stimulus event.
Predictive Analysis Settings

Data Sources

Water (mL) data was primarily collected using Fitbit. Fitbit makes activity tracking easy and automatic.

Distress data was primarily collected using QuantiModo. QuantiModo allows you to easily track mood, symptoms, or any outcome you want to optimize in a fraction of a second. You can also import your data from over 30 other apps and devices. QuantiModo then analyzes your data to identify which hidden factors are most likely to be influencing your mood or symptoms.


As with any human experiment, it was impossible to control for all potentially confounding variables. Correlation does not necessarily imply correlation. We can never know for sure if one factor is definitely the cause of an outcome. However, lack of correlation definitely implies the lack of a causal relationship. Hence, we can with great confidence rule out non-existent relationships. For instance, if we discover no relationship between mood and an antidepressant this information is just as or even more valuable than the discovery that there is a relationship.
We can also take advantage of several characteristics of time series data from many subjects to infer the likelihood of a causal relationship if we do find a correlational relationship. The criteria for causation are a group of minimal conditions necessary to provide adequate evidence of a causal relationship between an incidence and a possible consequence.

The list of the criteria is as follows:
Strength (A.K.A. Effect Size)
A small association does not mean that there is not a causal effect, though the larger the association, the more likely that it is causal. There is a very weakly positive relationship between Water intake and Distress

Consistency (A.K.A. Reproducibility)
Consistent findings observed by different persons in different places with different samples strengthens the likelihood of an effect. Furthermore, in accordance with the law of large numbers (LLN), the predictive power and accuracy of these results will continually grow over time. 55 paired data points were used in this analysis. Assuming that the relationship is merely coincidental, as the participant independently modifies their Water intake values, the observed strength of the relationship will decline until it is below the threshold of significance. To it another way, in the case that we do find a spurious correlation, suggesting that banana intake improves mood for instance, one will likely increase their banana intake. Due to the fact that this correlation is spurious, it is unlikely that you will see a continued and persistent corresponding increase in mood. So over time, the spurious correlation will naturally dissipate.

Causation is likely if a very specific population at a specific site and disease with no other likely explanation. The more specific an association between a factor and an effect is, the bigger the probability of a causal relationship.

The effect has to occur after the cause (and if there is an expected delay between the cause and expected effect, then the effect must occur after that delay). The confidence in a causal relationship is bolstered by the fact that time-precedence was taken into account in all calculations.

Biological Gradient
Greater exposure should generally lead to greater incidence of the effect. However, in some cases, the mere presence of the factor can trigger the effect. In other cases, an inverse proportion is observed: greater exposure leads to lower incidence.

A plausible bio-chemical mechanism between cause and effect is critical. This is where human brains excel. Based on our responses so far, 1 humans feel that there is a plausible mechanism of action and 0 feel that any relationship observed between Water intake and Distress is coincidental.

Coherence between epidemiological and laboratory findings increases the likelihood of an effect. It will be very enlightening to aggregate this data with the data from other participants with similar genetic, diseasomic, environmentomic, and demographic profiles.

All of human life can be considered a natural experiment. Occasionally, it is possible to appeal to experimental evidence.

The effect of similar factors may be considered.

Relationship Statistics

Property Value
Cause Variable Name Water intake
Effect Variable Name Distress
Sinn Predictive Coefficient 0.024881938427864
Confidence Level medium
Confidence Interval 0.59741877057243
Forward Pearson Correlation Coefficient 0.0882
Critical T Value 1.7527272727273
Total Water intake Over Previous 7 days Before ABOVE Average Distress 1 milliliters
Total Water intake Over Previous 7 days Before BELOW Average Distress 1977.9743969997 milliliters
Duration of Action 7 days
Effect Size very weakly positive
Number of Paired Measurements 55
Optimal Pearson Product 0.052549837298064
P Value 0.21476420933719
Statistical Significance 0.23227272815513
Strength of Relationship 0.59741877057243
Study Type population
Analysis Performed At 2019-02-03
Number of Participants 11

Water Statistics

Property Value
Variable Name Water (mL)
Aggregation Method SUM
Analysis Performed At 2019-01-28
Duration of Action 7 days
Kurtosis 204.60955518589
Mean 246.55070571942 milliliters
Median 208.90107913669 milliliters
Minimum Allowed Value 0 milliliters
Number of Correlations 135
Number of Measurements 16539
Onset Delay 30 minutes
Standard Deviation 192.76251202553
Unit Milliliters
UPC 075720004096
Variable ID 109592
Variance 143304.44930319

Distress Statistics

Property Value
Variable Name Distress
Aggregation Method MEAN
Analysis Performed At 2019-01-18
Duration of Action 24 hours
Kurtosis 2.8813080392775
Maximum Allowed Value 5 out of 5
Mean 2.4661185369318 out of 5
Median 2.4103051371244 out of 5
Minimum Allowed Value 1 out of 5
Number of Correlations 345
Number of Measurements 32940
Onset Delay 0 seconds
Standard Deviation 0.52551215340851
Unit 1 to 5 Rating
UPC 647297398818
Variable ID 1305
Variance 0.61453835687818 Principal Investigator - Mike Sinn